在生成性扩散模型中采用条件采样
Zheng Zhao1,2, Ziwei Luo1, Jens Sjölund1
1Department of Information Technology, Uppsala University, Uppsala, Sweden.
概括
生成性扩散模型现在可以对复杂问题 (如贝叶斯反向问题) 取样条件分布. 本审查涵盖了使用联合或边际分布的方法,以改进条件生成抽样.
科学领域:
- 机器学习 机器学习
- 计算统计学 计算统计学
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 生成性扩散模型是用于高维分布的强大的蒙特卡洛采样器.
- 目前在将这些模型应用于条件采样任务方面存在局限性,这对于贝叶斯反向问题至关重要.
研究的目的:
- 为生成扩散模型中的条件采样提供计算方法的全面审查.
- 突出构建条件生成样本的方法.
主要方法:
- 审查利用联合分配进行条件抽样的技术.
- 检查使用预训练的边际分布与明确的概率的检查方法.
- 专注于生成性扩散模型中的计算方法.
主要成果:
- 在生成性扩散模型中确定了条件采样的关键方法.
- 基于使用联合或边际分布的分类方法.
- 强调了某些边际分布方法中明确的概率的重要性.
结论:
- 生成性扩散模型中的条件采样是一个活跃的研究领域,有各种计算策略.
- 审查的方法提供了应用扩散模型到条件任务的途径,包括贝叶斯反向问题.
- 这一领域的进一步发展有望为复杂的科学挑战提供生成建模的进步.
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